AI Vs Gen AI

AI (Artificial Intelligence)

• Definition: A broad field of computer science focused on creating machines or systems that can perform tasks that usually require human intelligence.

• Scope: Covers all intelligent systems (from simple rule-based bots to advanced ML).

• Examples:

  • Rule-based systems (if–then logic in fraud detection).

  • Classical machine learning (predicting loan default using regression).

  • Computer vision (scanning cheque images).

  • Natural language processing (speech-to-text).

• Core Idea: Simulate human intelligence (learning, reasoning, problem-solving).

GenAI (Generative AI)

• Definition: A subset of AI that focuses on generating new content (text, images, code, music, video) based on training data.

• Technology: Uses advanced deep learning (especially transformer models, e.g., GPT, LLaMA, DALL·E, Stable Diffusion).

• Examples:

  • ChatGPT writing investment advice.

  • DALL·E generating synthetic KYC documents for testing.

  • GitHub Copilot suggesting Java microservice code.

• Core Idea: Create original-like output, not just analyze or classify.

Key Differences

Aspect	AI (General)	GenAI
Scope	Broad (rule-based → ML → DL → GenAI)	Subset of AI
Purpose	Decision-making, predictions, automation	Content creation (text, images, code, media)
Techniques	Rules, ML models, neural nets, optimization	Large language models, diffusion models, GANs
Output	Predictions, classifications, actions	New content resembling training data
Examples in BFSI	Fraud detection, credit scoring, chatbots	Auto-generating loan agreements, investor FAQs, synthetic customer data for testing

✅ So in short:

• AI = any system that mimics human intelligence.

• GenAI = a new wave of AI that creates content (text, images, code) using deep generative models.

AI vs GenAI in Banking – Use Case Comparison

Domain	AI (Traditional)	GenAI (Generative AI)
Customer Onboarding / KYC	- OCR + ML for ID verification. - Face recognition for video KYC. - Fraud detection using anomaly models.	- Generate synthetic KYC data for testing. - Auto-draft welcome emails and FAQs. - Summarize KYC regulations in natural language for staff.
Loan Processing	- Credit scoring models using ML. - Risk assessment & default prediction. - Workflow automation for approvals.	- Auto-generate loan agreements in plain English. - Provide personalized loan product explanations via chatbots. - Create synthetic loan applicant data for model training.
Fraud Detection & Compliance	- Transaction monitoring with ML. - Pattern recognition for fraud. - Rule engines for AML alerts.	- Auto-generate fraud investigation reports from raw logs. - Summarize AML/KYC compliance breaches for regulators. - Simulate new fraud patterns using synthetic data.
Wealth Management / Advisory	- Robo-advisors using ML to suggest portfolios. - Predictive models for market trends.	- Conversational GenAI advisor that explains investment strategy in simple terms. - Generate personalized investment newsletters. - Auto-create scenario simulations (e.g., “What if interest rates rise by 1%?”).
Operations & Support	- Chatbots for FAQs (rule-based or ML intent). - Predictive maintenance for ATMs.	- Natural conversation chatbots (ChatGPT-like). - Auto-generate support scripts, email replies, knowledge base articles.
Risk & Governance	- Predict credit, liquidity, and operational risk. - Classify risk events.	- Summarize risk heatmaps in plain English for CXOs. - Generate Board-level risk reports from raw data.

“AI in banking traditionally focused on predictions, classifications, and automation — for example, using ML for credit scoring or fraud detection. GenAI extends this by creating human-like content and insights. For instance, while AI predicts loan default risk, GenAI can auto-generate the loan agreement, provide personalized explanations to customers, and summarize compliance reports for regulators. Together, they deliver both intelligence and communication at scale.”

“In banking, we’ve been using AI for years — mainly for prediction and automation.For example, a credit scoring model that uses ML to predict whether a customer is likely to default, or fraud detection models that scan millions of transactions to flag anomalies. These are decision-support systems: they classify, predict, and automate.

Generative AI takes this a step further. Instead of just predicting, it can actually create human-like content. For instance, once a loan is approved, GenAI can automatically draft a loan agreement in plain English, summarize the terms for the customer in simple language, and even generate personalized financial advice in natural conversation.

Another example is in compliance: AI can flag suspicious transactions, but GenAI can automatically generate an AML investigation report or regulatory summary for auditors.

So, in short: AI gives us the intelligence to make decisions, while GenAI gives us the ability to communicate those decisions in a human-like, contextual way. Together, they transform BFSI from being data-driven to being both data-driven and customer-experience-driven.”